Abstract: Disclosed is an online optimization method for a power consumption policy of a terminal of Internet of things (IoT), including: performing comprehensive scanning on a user terminal and a link, and obtaining a link behavior graph; inputting the link behavior graph into a simulation based optimization (SBO) system to obtain power consumption parameters for minimizing power consumption of the user terminal; performing centralized storage on the link behavior graph and the power consumption parameters; regularly sending, by the user terminal, operation data to a server, and evaluating, by the server, whether a power consumption policy needs to be optimized; and issuing, by the server, a parameter update instruction to complete the upgrading of the power consumption policy of the user terminal. In the present disclosure, the update of the power consumption policy of the user terminal can be conveyed more quickly and more efficiently.
Type:
Grant
Filed:
July 25, 2024
Date of Patent:
December 17, 2024
Assignee:
Nanjing University of Information Science and Technology
Abstract: A multi-tag concurrent identification method and a system for a query tree based on feature groups are provided in this disclosure. In the disclosure, a whole data string space is divided into a plurality of disjoint subsets according to features of data strings returned by tags, where each of the subsets contains several different data strings, each of the data strings in the each of the subsets is regarded as a complete tag ID or a partial ID, and the each of the subsets corresponds to a unique query prefix, a length of the prefix is fixed and does not dynamically increase with an actual location of a collision, and when multiple data strings from a same subset return at a same time, a reader is capable of identifying them at a same time in a slot.
Type:
Grant
Filed:
August 15, 2023
Date of Patent:
April 23, 2024
Assignee:
Nanjing University of Information Science and Technology
Inventors:
Jian Su, Jialin Zhou, Wei Zhuang, Ling Tan
Abstract: The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into t
Type:
Grant
Filed:
September 16, 2022
Date of Patent:
December 5, 2023
Assignees:
Nanjing University of Information Science and Technology, National Climate Center